## [1] "There are 7 NA in the matrix X in Kelowna station"
##
## Call:
## lm(formula = y ~ ., data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -74.173 -28.423 -2.651 29.108 75.982
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 253.3923 6.4844 39.077 <2e-16 ***
## Month_1 -1.0241 0.7347 -1.394 0.1696
## Month_2 -2.0828 0.8270 -2.518 0.0151 *
## Month_3 0.4237 0.6457 0.656 0.5147
## Month_4 0.3979 0.7355 0.541 0.5910
## Month_5 0.8744 0.4300 2.033 0.0474 *
## Month_6 0.3296 0.4143 0.796 0.4300
## Month_7 0.6135 0.7436 0.825 0.4133
## Month_8 3.4547 2.1860 1.580 0.1205
## Month_9 2.2764 2.2905 0.994 0.3252
## Month_10 0.8146 2.4062 0.339 0.7364
## Month_11 0.8313 1.0375 0.801 0.4268
## Month_12 0.2330 0.7185 0.324 0.7472
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 39.01 on 49 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.3678, Adjusted R-squared: 0.213
## F-statistic: 2.376 on 12 and 49 DF, p-value: 0.0167
## [1] "There are 6 NA in the matrix X in Abbotsford station"
##
## Call:
## lm(formula = y ~ ., data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -76.591 -25.158 2.764 20.422 78.130
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 238.0161 6.2802 37.900 <2e-16 ***
## Month_1 0.2120 0.6940 0.306 0.7612
## Month_2 -0.9618 0.7972 -1.206 0.2333
## Month_3 1.1482 0.6190 1.855 0.0695 .
## Month_4 0.2776 0.4761 0.583 0.5625
## Month_5 0.6459 0.3066 2.106 0.0402 *
## Month_6 0.5502 0.2503 2.198 0.0326 *
## Month_7 0.5819 0.3767 1.545 0.1287
## Month_8 1.4800 0.8984 1.647 0.1058
## Month_9 2.1554 1.2924 1.668 0.1016
## Month_10 -2.0377 2.2403 -0.910 0.3674
## Month_11 -0.1693 1.5896 -0.107 0.9156
## Month_12 0.6542 0.8210 0.797 0.4293
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 37.57 on 50 degrees of freedom
## Multiple R-squared: 0.4017, Adjusted R-squared: 0.2581
## F-statistic: 2.798 on 12 and 50 DF, p-value: 0.00537
## [1] "There are 7 NA in the matrix X in Kelowna station"
##
## Call:
## lm(formula = y ~ ., data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -42.414 -10.065 -0.746 10.162 41.305
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.15763 2.97528 -0.725 0.472
## Month_1 -0.21288 0.33713 -0.631 0.531
## Month_2 -0.14369 0.37948 -0.379 0.707
## Month_3 -0.10422 0.29626 -0.352 0.726
## Month_4 0.25659 0.33749 0.760 0.451
## Month_5 0.05314 0.19732 0.269 0.789
## Month_6 0.01764 0.19008 0.093 0.926
## Month_7 0.22220 0.34117 0.651 0.518
## Month_8 -1.37895 1.00303 -1.375 0.175
## Month_9 -0.70133 1.05095 -0.667 0.508
## Month_10 0.24395 1.10405 0.221 0.826
## Month_11 0.31184 0.47602 0.655 0.515
## Month_12 0.29870 0.32969 0.906 0.369
##
## Residual standard error: 17.9 on 49 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.1383, Adjusted R-squared: -0.07268
## F-statistic: 0.6556 on 12 and 49 DF, p-value: 0.784
## [1] "There are 7 NA in the matrix X in Kelowna station"
##
## Call:
## lm(formula = y ~ ., data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -44.448 -7.620 -0.728 8.497 40.598
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.95467 2.99008 -0.654 0.516
## Month_1 -0.15930 0.33505 -0.475 0.637
## Month_2 -0.04336 0.36974 -0.117 0.907
## Month_3 0.02127 0.30023 0.071 0.944
## Month_4 0.12031 0.34145 0.352 0.726
## Month_5 -0.08646 0.20025 -0.432 0.668
## Month_6 0.04892 0.18545 0.264 0.793
## Month_7 0.16642 0.34576 0.481 0.633
## Month_8 -1.33634 1.02896 -1.299 0.200
## Month_9 -0.69091 1.04250 -0.663 0.511
## Month_10 0.05866 1.08950 0.054 0.957
## Month_11 0.18527 0.46411 0.399 0.692
## Month_12 0.23704 0.32482 0.730 0.469
##
## Residual standard error: 17.35 on 47 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.09791, Adjusted R-squared: -0.1324
## F-statistic: 0.4251 on 12 and 47 DF, p-value: 0.9456
## [1] "There are 7 NA in the matrix X in Kelowna station"
## [[1]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -66.805 -32.650 -4.853 31.271 107.397
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 256.3213 5.5035 46.574 <2e-16 ***
## Predictor -1.2670 0.6753 -1.876 0.0654 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 42.76 on 61 degrees of freedom
## Multiple R-squared: 0.05456, Adjusted R-squared: 0.03907
## F-statistic: 3.521 on 1 and 61 DF, p-value: 0.0654
##
##
## [[2]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -81.970 -31.565 -5.315 34.240 106.594
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 252.4300 5.6819 44.427 < 2e-16 ***
## Predictor -1.9819 0.7303 -2.714 0.00864 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 41.54 on 61 degrees of freedom
## Multiple R-squared: 0.1077, Adjusted R-squared: 0.09309
## F-statistic: 7.364 on 1 and 61 DF, p-value: 0.008637
##
##
## [[3]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -75.647 -32.060 1.217 31.758 111.414
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 258.3407 5.5563 46.495 <2e-16 ***
## Predictor 0.1299 0.6231 0.208 0.836
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 43.96 on 61 degrees of freedom
## Multiple R-squared: 0.0007119, Adjusted R-squared: -0.01567
## F-statistic: 0.04346 on 1 and 61 DF, p-value: 0.8356
##
##
## [[4]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -78.125 -33.301 4.284 30.903 98.031
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 259.4956 5.5654 46.627 <2e-16 ***
## Predictor 0.8320 0.7418 1.122 0.266
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 43.53 on 61 degrees of freedom
## Multiple R-squared: 0.02021, Adjusted R-squared: 0.004144
## F-statistic: 1.258 on 1 and 61 DF, p-value: 0.2664
##
##
## [[5]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -75.396 -35.001 1.673 35.634 83.519
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 255.8012 5.4512 46.925 <2e-16 ***
## Predictor 0.9700 0.4313 2.249 0.0281 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 42.26 on 61 degrees of freedom
## Multiple R-squared: 0.07657, Adjusted R-squared: 0.06143
## F-statistic: 5.058 on 1 and 61 DF, p-value: 0.02813
##
##
## [[6]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -83.556 -29.442 0.235 27.432 108.720
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 258.2837 5.3297 48.462 <2e-16 ***
## Predictor 0.7456 0.3357 2.221 0.0301 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 42.3 on 61 degrees of freedom
## Multiple R-squared: 0.0748, Adjusted R-squared: 0.05963
## F-statistic: 4.932 on 1 and 61 DF, p-value: 0.03009
##
##
## [[7]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -93.47 -29.47 4.48 31.00 110.20
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 257.4176 5.4499 47.233 <2e-16 ***
## Predictor 1.0818 0.6433 1.681 0.0978 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 42.99 on 61 degrees of freedom
## Multiple R-squared: 0.0443, Adjusted R-squared: 0.02863
## F-statistic: 2.827 on 1 and 61 DF, p-value: 0.09778
##
##
## [[8]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -82.046 -32.733 5.984 26.482 90.138
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 261.533 5.610 46.620 <2e-16 ***
## Predictor 4.142 2.137 1.939 0.0572 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 42.68 on 61 degrees of freedom
## Multiple R-squared: 0.05804, Adjusted R-squared: 0.0426
## F-statistic: 3.759 on 1 and 61 DF, p-value: 0.05716
##
##
## [[9]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -78.43 -32.16 2.58 31.56 110.58
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 259.492 5.901 43.973 <2e-16 ***
## Predictor 1.219 2.375 0.513 0.61
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 43.88 on 61 degrees of freedom
## Multiple R-squared: 0.004298, Adjusted R-squared: -0.01202
## F-statistic: 0.2633 on 1 and 61 DF, p-value: 0.6097
##
##
## [[10]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -75.271 -31.428 0.683 29.028 113.409
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 255.326 6.155 41.484 <2e-16 ***
## Predictor -2.654 2.384 -1.113 0.27
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 43.54 on 61 degrees of freedom
## Multiple R-squared: 0.01991, Adjusted R-squared: 0.003839
## F-statistic: 1.239 on 1 and 61 DF, p-value: 0.27
##
##
## [[11]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -75.894 -33.908 4.637 32.147 113.396
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 258.8379 5.5353 46.761 <2e-16 ***
## Predictor 1.4081 0.9497 1.483 0.143
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 43.55 on 60 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.03534, Adjusted R-squared: 0.01926
## F-statistic: 2.198 on 1 and 60 DF, p-value: 0.1434
##
##
## [[12]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -74.048 -32.027 1.627 31.474 110.242
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 258.4799 5.5688 46.416 <2e-16 ***
## Predictor 0.0652 0.7908 0.082 0.935
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 43.97 on 61 degrees of freedom
## Multiple R-squared: 0.0001114, Adjusted R-squared: -0.01628
## F-statistic: 0.006797 on 1 and 61 DF, p-value: 0.9346
## [1] "There are 7 NA in the matrix X in Kelowna station"
## [[1]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -50.225 -7.144 -0.725 8.355 43.760
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.3438 2.1256 -0.162 0.872
## Predictor -0.1347 0.2604 -0.517 0.607
##
## Residual standard error: 16.32 on 59 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.004518, Adjusted R-squared: -0.01235
## F-statistic: 0.2678 on 1 and 59 DF, p-value: 0.6067
##
##
## [[2]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -52.267 -6.634 0.202 7.564 45.013
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.7947 2.2509 -0.353 0.725
## Predictor -0.2197 0.2866 -0.767 0.446
##
## Residual standard error: 16.28 on 59 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.009863, Adjusted R-squared: -0.006919
## F-statistic: 0.5877 on 1 and 59 DF, p-value: 0.4464
##
##
## [[3]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -50.053 -7.076 -0.468 7.661 43.762
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.06525 2.09766 -0.031 0.975
## Predictor -0.10831 0.23333 -0.464 0.644
##
## Residual standard error: 16.33 on 59 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.003639, Adjusted R-squared: -0.01325
## F-statistic: 0.2155 on 1 and 59 DF, p-value: 0.6442
##
##
## [[4]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -52.165 -6.767 -0.976 8.876 44.489
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.01599 2.13357 0.007 0.994
## Predictor 0.11011 0.28919 0.381 0.705
##
## Residual standard error: 16.34 on 59 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.002451, Adjusted R-squared: -0.01446
## F-statistic: 0.145 on 1 and 59 DF, p-value: 0.7048
##
##
## [[5]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -51.920 -6.577 -1.282 8.549 43.534
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.03528 2.13393 0.017 0.987
## Predictor -0.07384 0.17494 -0.422 0.675
##
## Residual standard error: 16.34 on 59 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.00301, Adjusted R-squared: -0.01389
## F-statistic: 0.1781 on 1 and 59 DF, p-value: 0.6745
##
##
## [[6]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -51.145 -6.495 -0.900 8.014 44.608
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.16879 2.08624 -0.081 0.936
## Predictor 0.09148 0.12955 0.706 0.483
##
## Residual standard error: 16.29 on 59 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.008381, Adjusted R-squared: -0.008426
## F-statistic: 0.4987 on 1 and 59 DF, p-value: 0.4829
##
##
## [[7]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -51.690 -7.461 -0.860 8.699 44.493
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.2249 2.0989 -0.107 0.915
## Predictor 0.1123 0.2489 0.451 0.653
##
## Residual standard error: 16.33 on 59 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.00344, Adjusted R-squared: -0.01345
## F-statistic: 0.2037 on 1 and 59 DF, p-value: 0.6534
##
##
## [[8]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -46.387 -7.871 -0.618 8.411 40.488
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.2774 2.1858 -0.584 0.561
## Predictor -1.2829 0.8431 -1.522 0.133
##
## Residual standard error: 16.05 on 59 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.03776, Adjusted R-squared: 0.02145
## F-statistic: 2.315 on 1 and 59 DF, p-value: 0.1334
##
##
## [[9]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -50.851 -7.678 0.699 7.647 44.001
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.2263 2.2283 -0.550 0.584
## Predictor -1.1594 0.8948 -1.296 0.200
##
## Residual standard error: 16.13 on 59 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.02766, Adjusted R-squared: 0.01118
## F-statistic: 1.679 on 1 and 59 DF, p-value: 0.2002
##
##
## [[10]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -51.833 -6.907 -1.584 8.155 44.254
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.04718 2.35924 -0.020 0.984
## Predictor 0.07959 0.89926 0.089 0.930
##
## Residual standard error: 16.36 on 59 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.0001328, Adjusted R-squared: -0.01681
## F-statistic: 0.007834 on 1 and 59 DF, p-value: 0.9298
##
##
## [[11]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -53.555 -6.350 -1.233 8.015 43.287
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.2708 2.1088 -0.128 0.898
## Predictor 0.3270 0.3591 0.910 0.366
##
## Residual standard error: 16.33 on 58 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.01409, Adjusted R-squared: -0.00291
## F-statistic: 0.8288 on 1 and 58 DF, p-value: 0.3664
##
##
## [[12]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -51.438 -6.220 -0.707 8.187 44.789
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05742 2.08668 0.028 0.978
## Predictor 0.29648 0.29543 1.004 0.320
##
## Residual standard error: 16.22 on 59 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.01678, Adjusted R-squared: 0.000119
## F-statistic: 1.007 on 1 and 59 DF, p-value: 0.3197
## [1] "There are 7 NA in the matrix X in Kelowna station"
## [[1]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -48.992 -9.255 -1.606 11.268 43.006
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.4578 2.2363 -0.205 0.838
## Predictor -0.2100 0.2744 -0.765 0.447
##
## Residual standard error: 17.38 on 61 degrees of freedom
## Multiple R-squared: 0.009509, Adjusted R-squared: -0.006729
## F-statistic: 0.5856 on 1 and 61 DF, p-value: 0.4471
##
##
## [[2]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -52.275 -10.258 -0.622 10.410 45.131
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.2857 2.3564 -0.546 0.587
## Predictor -0.3889 0.3029 -1.284 0.204
##
## Residual standard error: 17.23 on 61 degrees of freedom
## Multiple R-squared: 0.02631, Adjusted R-squared: 0.01035
## F-statistic: 1.648 on 1 and 61 DF, p-value: 0.204
##
##
## [[3]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -47.713 -9.686 -0.877 10.913 42.753
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05739 2.19069 0.026 0.979
## Predictor -0.23172 0.24567 -0.943 0.349
##
## Residual standard error: 17.33 on 61 degrees of freedom
## Multiple R-squared: 0.01438, Adjusted R-squared: -0.001783
## F-statistic: 0.8897 on 1 and 61 DF, p-value: 0.3493
##
##
## [[4]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -52.210 -9.899 -0.031 12.005 44.366
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2008 2.2200 0.090 0.928
## Predictor 0.2417 0.2959 0.817 0.417
##
## Residual standard error: 17.36 on 61 degrees of freedom
## Multiple R-squared: 0.01082, Adjusted R-squared: -0.005394
## F-statistic: 0.6674 on 1 and 61 DF, p-value: 0.4171
##
##
## [[5]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -51.522 -9.357 -1.115 11.263 43.954
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.23107 2.25086 -0.103 0.919
## Predictor 0.04543 0.17809 0.255 0.799
##
## Residual standard error: 17.45 on 61 degrees of freedom
## Multiple R-squared: 0.001066, Adjusted R-squared: -0.01531
## F-statistic: 0.06508 on 1 and 61 DF, p-value: 0.7995
##
##
## [[6]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -50.773 -11.461 -1.726 12.033 44.086
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.1287 2.1895 -0.059 0.953
## Predictor 0.1040 0.1379 0.754 0.454
##
## Residual standard error: 17.38 on 61 degrees of freedom
## Multiple R-squared: 0.009234, Adjusted R-squared: -0.007008
## F-statistic: 0.5685 on 1 and 61 DF, p-value: 0.4537
##
##
## [[7]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -51.394 -9.848 -1.025 11.930 43.917
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.2071 2.2102 -0.094 0.926
## Predictor 0.1058 0.2609 0.405 0.687
##
## Residual standard error: 17.43 on 61 degrees of freedom
## Multiple R-squared: 0.002688, Adjusted R-squared: -0.01366
## F-statistic: 0.1644 on 1 and 61 DF, p-value: 0.6866
##
##
## [[8]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -45.680 -9.609 -2.472 12.374 39.303
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.1762 2.2439 -0.524 0.6021
## Predictor -1.4279 0.8546 -1.671 0.0999 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 17.07 on 61 degrees of freedom
## Multiple R-squared: 0.04376, Adjusted R-squared: 0.02809
## F-statistic: 2.792 on 1 and 61 DF, p-value: 0.09988
##
##
## [[9]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -50.396 -10.343 0.704 10.899 43.290
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.3835 2.3008 -0.601 0.550
## Predictor -1.4685 0.9262 -1.586 0.118
##
## Residual standard error: 17.11 on 61 degrees of freedom
## Multiple R-squared: 0.03959, Adjusted R-squared: 0.02384
## F-statistic: 2.514 on 1 and 61 DF, p-value: 0.118
##
##
## [[10]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -51.598 -9.754 -1.585 11.719 43.459
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.19548 2.46798 -0.079 0.937
## Predictor -0.07488 0.95599 -0.078 0.938
##
## Residual standard error: 17.46 on 61 degrees of freedom
## Multiple R-squared: 0.0001006, Adjusted R-squared: -0.01629
## F-statistic: 0.006135 on 1 and 61 DF, p-value: 0.9378
##
##
## [[11]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -53.940 -12.010 -0.993 11.476 42.381
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.3056 2.1879 -0.140 0.889
## Predictor 0.4591 0.3754 1.223 0.226
##
## Residual standard error: 17.21 on 60 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.02432, Adjusted R-squared: 0.008063
## F-statistic: 1.496 on 1 and 60 DF, p-value: 0.2261
##
##
## [[12]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -51.09 -10.55 -0.76 11.15 44.31
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1412 2.1884 0.065 0.949
## Predictor 0.3489 0.3108 1.123 0.266
##
## Residual standard error: 17.28 on 61 degrees of freedom
## Multiple R-squared: 0.02024, Adjusted R-squared: 0.004183
## F-statistic: 1.26 on 1 and 61 DF, p-value: 0.266
## [1] "There are 1 NA in the matrix X in Kelowna station"
## [1] "There are 1 NA in the matrix X in Kelowna station"
## [1] "There are 1 NA in the matrix X in Kelowna station"
## $Winter
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -70.023 -36.364 -5.873 29.224 111.165
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 263.636 5.783 45.588 <2e-16 ***
## Predictor -1.361 0.595 -2.288 0.0256 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 42.2 on 61 degrees of freedom
## Multiple R-squared: 0.07902, Adjusted R-squared: 0.06393
## F-statistic: 5.234 on 1 and 61 DF, p-value: 0.02563
##
##
## $Spring
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -79.948 -32.174 -0.383 33.268 87.311
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 250.4780 6.3037 39.735 <2e-16 ***
## Predictor 1.0201 0.4361 2.339 0.0226 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 42.13 on 61 degrees of freedom
## Multiple R-squared: 0.08232, Adjusted R-squared: 0.06727
## F-statistic: 5.472 on 1 and 61 DF, p-value: 0.02262
##
##
## $Summer
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -84.20 -28.34 1.48 28.05 112.02
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 253.6918 5.7381 44.212 <2e-16 ***
## Predictor 0.7858 0.3530 2.226 0.0297 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 42.29 on 61 degrees of freedom
## Multiple R-squared: 0.07515, Adjusted R-squared: 0.05999
## F-statistic: 4.957 on 1 and 61 DF, p-value: 0.02969
##
##
## $Fall
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -76.229 -33.578 4.456 31.865 113.254
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 255.8871 5.7324 44.639 <2e-16 ***
## Predictor 1.4104 0.9849 1.432 0.157
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 43.26 on 61 degrees of freedom
## Multiple R-squared: 0.03252, Adjusted R-squared: 0.01666
## F-statistic: 2.051 on 1 and 61 DF, p-value: 0.1572
## $Winter
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -51.644 -6.370 -1.502 7.853 44.049
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.01724 2.28755 -0.008 0.994
## Predictor -0.03184 0.23244 -0.137 0.892
##
## Residual standard error: 16.36 on 59 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.000318, Adjusted R-squared: -0.01663
## F-statistic: 0.01877 on 1 and 59 DF, p-value: 0.8915
##
##
## $Spring
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -50.090 -7.713 -1.469 7.909 42.323
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.1801 2.4422 0.483 0.631
## Predictor -0.1778 0.1728 -1.029 0.308
##
## Residual standard error: 16.22 on 59 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.01763, Adjusted R-squared: 0.0009797
## F-statistic: 1.059 on 1 and 59 DF, p-value: 0.3077
##
##
## $Summer
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -51.676 -7.304 -0.743 8.442 44.729
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.56134 2.24189 -0.250 0.803
## Predictor 0.07037 0.13652 0.515 0.608
##
## Residual standard error: 16.32 on 59 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.004483, Adjusted R-squared: -0.01239
## F-statistic: 0.2657 on 1 and 59 DF, p-value: 0.6082
##
##
## $Fall
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -52.661 -8.028 -1.185 8.051 43.888
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.5305 2.1953 -0.242 0.810
## Predictor 0.2133 0.3720 0.573 0.568
##
## Residual standard error: 16.31 on 59 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.005543, Adjusted R-squared: -0.01131
## F-statistic: 0.3289 on 1 and 59 DF, p-value: 0.5685
## $Winter
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -50.860 -8.733 -1.768 12.110 43.269
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2931 2.3887 0.123 0.903
## Predictor -0.1049 0.2458 -0.427 0.671
##
## Residual standard error: 17.43 on 61 degrees of freedom
## Multiple R-squared: 0.002976, Adjusted R-squared: -0.01337
## F-statistic: 0.1821 on 1 and 61 DF, p-value: 0.6711
##
##
## $Spring
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -50.689 -10.185 -1.982 11.750 42.587
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.61273 2.60676 0.235 0.815
## Predictor -0.09239 0.18034 -0.512 0.610
##
## Residual standard error: 17.42 on 61 degrees of freedom
## Multiple R-squared: 0.004284, Adjusted R-squared: -0.01204
## F-statistic: 0.2625 on 1 and 61 DF, p-value: 0.6103
##
##
## $Summer
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -51.392 -10.019 -1.273 11.883 44.143
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.52160 2.36443 -0.221 0.826
## Predictor 0.06858 0.14544 0.472 0.639
##
## Residual standard error: 17.43 on 61 degrees of freedom
## Multiple R-squared: 0.003632, Adjusted R-squared: -0.0127
## F-statistic: 0.2224 on 1 and 61 DF, p-value: 0.6389
##
##
## $Fall
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -52.539 -11.274 -1.793 11.968 43.258
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.5690 2.3058 -0.247 0.806
## Predictor 0.2555 0.3962 0.645 0.521
##
## Residual standard error: 17.4 on 61 degrees of freedom
## Multiple R-squared: 0.006771, Adjusted R-squared: -0.009512
## F-statistic: 0.4158 on 1 and 61 DF, p-value: 0.5214
## [1] "There are 1 NA in the matrix X in Kelowna station"
## [1] "There are 1 NA in the matrix X in Kelowna station"
## [1] "There are 1 NA in the matrix X in Kelowna station"
## $Winter
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -74.29 -32.00 1.57 31.56 110.58
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 258.25889 22.85873 11.298 <2e-16 ***
## Predictor 0.02761 3.51045 0.008 0.994
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 43.98 on 61 degrees of freedom
## Multiple R-squared: 1.014e-06, Adjusted R-squared: -0.01639
## F-statistic: 6.187e-05 on 1 and 61 DF, p-value: 0.9937
##
##
## $Spring
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -79.756 -32.677 2.952 31.731 102.718
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 210.190 53.523 3.927 0.000221 ***
## Predictor 2.585 2.853 0.906 0.368412
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 43.68 on 61 degrees of freedom
## Multiple R-squared: 0.01328, Adjusted R-squared: -0.002893
## F-statistic: 0.8211 on 1 and 61 DF, p-value: 0.3684
##
##
## $Summer
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -94.147 -31.824 5.002 30.507 101.871
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 101.352 81.736 1.240 0.2197
## Predictor 6.232 3.236 1.926 0.0588 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 42.7 on 61 degrees of freedom
## Multiple R-squared: 0.05732, Adjusted R-squared: 0.04187
## F-statistic: 3.709 on 1 and 61 DF, p-value: 0.05877
##
##
## $Fall
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -74.804 -32.114 1.731 31.313 110.445
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 254.4511 57.6007 4.418 4.16e-05 ***
## Predictor 0.2042 2.9392 0.069 0.945
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 43.97 on 61 degrees of freedom
## Multiple R-squared: 7.908e-05, Adjusted R-squared: -0.01631
## F-statistic: 0.004824 on 1 and 61 DF, p-value: 0.9449
## $Winter
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -51.459 -9.034 -0.272 8.711 45.050
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -10.889 8.426 -1.292 0.201
## Predictor 1.696 1.290 1.315 0.193
##
## Residual standard error: 16.13 on 59 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.02849, Adjusted R-squared: 0.01203
## F-statistic: 1.73 on 1 and 59 DF, p-value: 0.1934
##
##
## $Spring
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -51.933 -6.375 -1.050 8.216 44.243
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.9754 20.6209 0.193 0.848
## Predictor -0.2209 1.1001 -0.201 0.842
##
## Residual standard error: 16.35 on 59 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.0006827, Adjusted R-squared: -0.01625
## F-statistic: 0.04031 on 1 and 59 DF, p-value: 0.8416
##
##
## $Summer
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -51.634 -6.823 -1.525 7.821 43.873
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.3832 32.0116 0.199 0.843
## Predictor -0.2596 1.2706 -0.204 0.839
##
## Residual standard error: 16.35 on 59 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.000707, Adjusted R-squared: -0.01623
## F-statistic: 0.04175 on 1 and 59 DF, p-value: 0.8388
##
##
## $Fall
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -50.007 -7.094 -0.244 6.376 45.517
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 46.089 21.577 2.136 0.0368 *
## Predictor -2.380 1.106 -2.152 0.0355 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.75 on 59 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.07278, Adjusted R-squared: 0.05707
## F-statistic: 4.631 on 1 and 59 DF, p-value: 0.0355
## $Winter
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -51.337 -10.449 -0.956 12.778 44.128
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.975 9.029 -0.772 0.443
## Predictor 1.087 1.387 0.784 0.436
##
## Residual standard error: 17.37 on 61 degrees of freedom
## Multiple R-squared: 0.009974, Adjusted R-squared: -0.006256
## F-statistic: 0.6145 on 1 and 61 DF, p-value: 0.4361
##
##
## $Spring
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -51.420 -9.242 -1.184 11.148 43.381
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.0048 21.3667 -0.375 0.709
## Predictor 0.4231 1.1388 0.372 0.712
##
## Residual standard error: 17.44 on 61 degrees of freedom
## Multiple R-squared: 0.002258, Adjusted R-squared: -0.0141
## F-statistic: 0.1381 on 1 and 61 DF, p-value: 0.7115
##
##
## $Summer
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -51.151 -10.080 -1.512 12.001 42.978
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 13.2976 33.3766 0.398 0.692
## Predictor -0.5319 1.3213 -0.403 0.689
##
## Residual standard error: 17.44 on 61 degrees of freedom
## Multiple R-squared: 0.002649, Adjusted R-squared: -0.0137
## F-statistic: 0.162 on 1 and 61 DF, p-value: 0.6887
##
##
## $Fall
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -49.620 -8.788 0.704 10.593 44.956
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 54.841 21.748 2.522 0.0143 *
## Predictor -2.817 1.110 -2.538 0.0137 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 16.6 on 61 degrees of freedom
## Multiple R-squared: 0.09553, Adjusted R-squared: 0.08071
## F-statistic: 6.443 on 1 and 61 DF, p-value: 0.01371
## [1] "Year" "Season" "station"
## [4] "maxsea_Mean_Temp" "maxsea_Percentile_95" "maxsea_EHF_95"
